import pandas as pd
import pandas_ta as ta
from statsmodels.nonparametric.kernel_regression import KernelReg
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import datetime
import matplotlib.pyplot as plt
from backtesting import Strategy
from backtesting import Backtest
import seaborn as sns

df = pd.read_csv("EURUSD_Candlestick_15_M_BID_01.02.2023-17.02.2024.csv") # read in the data
df["Gmt time"]=df["Gmt time"].str.replace(".000","", regex=False) # remove the milliseconds; mention that we are not using regex but a simple string replace
df['Gmt time']=pd.to_datetime(df['Gmt time'],format='%d.%m.%Y %H:%M:%S') # convert the time string to datetime object
df=df[df.High!=df.Low] # remove rows where High=Low

df["EMA_slow"]=ta.ema(df.Close, length=50)
df["EMA_fast"]=ta.ema(df.Close, length=40)
df['ATR']=ta.atr(df.High, df.Low, df.Close, length=7)
df

dfsample = df[0:] # create a shadow coppy of the df. For simplicity, we can use part of the data.

X = dfsample.index # extract the index (time) of df and store it as variable X
# initialise the kernel regression model for nadaraya-watson kernel regression
# endog is the dependent variable, exog is the independent variable, var_type is the type of the variables, reg_type is the type of regression, bw is the bandwidth
# - Endogenous (dependent) variable: Close price
# - Exogenous (independent) variable: Time
# - Variable type: 'c' for continuous
# - Regression type: 'lc' for local constant another option is 'll' for local linear
# - Bandwidth: 3; the bandwidth is the window size of the kernel, a larger bandwidth will smooth the curve more a smaller bandwidth will make the curve more wiggly following the current data more closely
model = KernelReg(endog=dfsample['Close'], exog=dfsample.index, var_type='c', reg_type='lc', bw=[3])
# - fittd_values is the predicted values of the Close price
# - marginal_effects is the marginal effects in the Close price. I.e how a small change in the time will affect the Close price
fitted_values, marginal_effects = model.fit() # fit the model


# Add fitted values to DataFrame
dfsample['NW_Fitted'] = fitted_values

# Calculate residuals
# difference between the actual Close price and the predicted Close price; useful in understanding how well the model fits the data
residuals = dfsample['Close'] - fitted_values

# Calculate standard deviation of residuals
# standard deviation of the residuals is a measure of the spread of the residuals
# it is used to calculate the upper and lower envelopes
std_dev = 2.*np.std(residuals)
#std_dev = dfsample['Close'].rolling(window=30).std()

# Calculate upper and lower envelopes
dfsample['Upper_Envelope'] = dfsample['NW_Fitted'] + std_dev
dfsample['Lower_Envelope'] = dfsample['NW_Fitted'] - std_dev

dfsample[0:100]

/tmp/ipykernel_11488/2884411395.py:18: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dfsample['NW_Fitted'] = fitted_values
/tmp/ipykernel_11488/2884411395.py:31: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dfsample['Upper_Envelope'] = dfsample['NW_Fitted'] + std_dev
/tmp/ipykernel_11488/2884411395.py:32: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dfsample['Lower_Envelope'] = dfsample['NW_Fitted'] - std_dev

my_bbands = ta.bbands(dfsample.Close, length=30, std=2) # calculate the bollinger bands for the closing prices; lenght 30 means it looks at last 30 bars, std=2 means it uses 2 standard deviations
dfsample=dfsample.join(my_bbands)
#1.105424	1.097329

dfsample.columns

Index(['Gmt time', 'Open', 'High', 'Low', 'Close', 'Volume', 'EMA_slow',
       'EMA_fast', 'ATR', 'NW_Fitted', 'Upper_Envelope', 'Lower_Envelope',
       'BBL_30_2.0', 'BBM_30_2.0', 'BBU_30_2.0', 'BBB_30_2.0', 'BBP_30_2.0'],
      dtype='object')

# Create a plot with 1 row
fig = make_subplots(rows=1, cols=1)

# Add candlestick plot with customized line colors
fig.add_trace(go.Candlestick(x=dfsample.index,
                             open=dfsample['Open'],
                             high=dfsample['High'],
                             low=dfsample['Low'],
                             close=dfsample['Close'],
                             increasing=dict(line=dict(color='rgba(0, 255, 0, 0.6)', width=0.1), # Red with transparency for increasing
                                             fillcolor='rgba(0, 255, 0, 0.6)'),  # Match fill color with line color
                             decreasing=dict(line=dict(color='rgba(255, 0, 0, 0.6)', width=0.1), # Green with transparency for decreasing
                                             fillcolor='rgba(255, 0, 0, 0.6)')), # Match fill color with line color
              row=1, col=1)

# Add Nadaraya-Watson fitted line
fig.add_trace(go.Scatter(x=dfsample.index, y=dfsample['NW_Fitted'],
                         line=dict(color='green', width=2),
                         name="Nadaraya-Watson Fit"),
              row=1, col=1)

# Add upper standard deviation envelope
fig.add_trace(go.Scatter(x=dfsample.index, y=dfsample['Upper_Envelope'],
                         line=dict(color='rgba(0,0,255,0.2)'), # Light blue color
                         name='Upper Envelope',
                         showlegend=False),
              row=1, col=1)

# Add lower standard deviation envelope
fig.add_trace(go.Scatter(x=dfsample.index, y=dfsample['Lower_Envelope'],
                         line=dict(color='rgba(0,0,255,0.2)'), # Light blue color
                         name='Lower Envelope',
                         fill='tonexty', # This fills the area between this trace and the next trace
                         fillcolor='rgba(0,0,255,0.3)', # More opaque blue fill
                         showlegend=False),
              row=1, col=1)


# # Add Bollinger Bands
# fig.add_trace(go.Scatter(x=dfsample.index, y=dfsample['BBU_30_2.0'],
#                          line=dict(color='rgba(0, 0, 255, 0.4)'),  # Upper Band
#                          name='Upper Bollinger Band',
#                          showlegend=True),
#               row=1, col=1)

# fig.add_trace(go.Scatter(x=dfsample.index, y=dfsample['BBM_30_2.0'],
#                          line=dict(color='rgba(0, 0, 255, 0.6)'),  # Middle Band
#                          name='Middle Bollinger Band',
#                          showlegend=True),
#               row=1, col=1)

# fig.add_trace(go.Scatter(x=dfsample.index, y=dfsample['BBL_30_2.0'],
#                          line=dict(color='rgba(0, 0, 255, 0.4)'),  # Lower Band
#                          name='Lower Bollinger Band',
#                          fill='tonexty',  # Fill between this line and the next upper band line
#                          fillcolor='rgba(0, 0, 255, 0.1)',  # Light blue fill between the bands
#                          showlegend=True),
#               row=1, col=1)


# Update layout to set background color to black and remove gridlines
fig.update_layout(
    width=1500,
    height=1200,
    sliders=[],
    paper_bgcolor='black',  # Set the background color of the entire figure
    plot_bgcolor='black',   # Set the background color of the plotting area
    xaxis_showgrid=False,   # Remove x-axis gridlines
    yaxis_showgrid=False,   # Remove y-axis gridlines
)

# Show the plot
fig.show()

def ema_signal(df, backcandles):
    # Create boolean Series for conditions
    above = df['EMA_fast'] > df['EMA_slow'] # is the fast EMA above the slow EMA (bullish)
    below = df['EMA_fast'] < df['EMA_slow'] # is the fast EMA below the slow EMA (bearish)

    # Rolling window to check if condition is met consistently over the window
    above_all = above.rolling(window=backcandles).apply(lambda x: x.all(), raw=True).fillna(0).astype(bool)
    below_all = below.rolling(window=backcandles).apply(lambda x: x.all(), raw=True).fillna(0).astype(bool)

    # Assign signals based on conditions (if the fast EMA is above or below the slow EMA)
    df['EMASignal'] = 0  # Default no signal
    df.loc[above_all, 'EMASignal'] = 2  # Signal 2 where EMA_fast consistently above EMA_slow (buy signal)
    df.loc[below_all, 'EMASignal'] = 1  # Signal 1 where EMA_fast consistently below EMA_slow (sell signal)

    return df # return the dataframe which at this point will contain the signal.

df = df[-60000:] # limit the dataframe to last 60k entries.
df.reset_index(inplace=True, drop=True) # because we selected a subset of the data, we need to reset the index
# TODO: check if dfsample is the correct dataframe to use.
df = ema_signal(dfsample,  7) # get signals from the EMA strategy using 7 backcandles (7x5minute = 35 minutes)

df

def total_signal(df):
    # Vectorized conditions for total_signal (buy when EMA signal is 2 and close price is below lower envelope, sell when EMA signal is 1 and close price is above upper envelope)
    condition_buy = (df['EMASignal'] == 2) & (df['Close'] <= df['Lower_Envelope'])
    condition_sell = (df['EMASignal'] == 1) & (df['Close'] >= df['Upper_Envelope'])

    # Assigning signals based on conditions
    df['Total_Signal'] = 0  # Default no signal
    df.loc[condition_buy, 'Total_Signal'] = 2
    df.loc[condition_sell, 'Total_Signal'] = 1

total_signal(dfsample)

# Count the signals
dfsample["TotalSignal"]=dfsample.Total_Signal
dfsample.TotalSignal.value_counts()

0    17849
2      183
1      144
Name: TotalSignal, dtype: int64

def pointpos(x):
    if x['TotalSignal']==2:
        return x['Low']-1e-4
    elif x['TotalSignal']==1:
        return x['High']+1e-4
    else:
        return np.nan

df['pointpos'] = df.apply(lambda row: pointpos(row), axis=1)

dfpl = df

# Create a plot with 2 rows
fig = make_subplots(rows=2, cols=1)

# Add candlestick plot on the first row
fig.add_trace(go.Candlestick(x=dfpl.index,
                             open=dfpl['Open'],
                             high=dfpl['High'],
                             low=dfpl['Low'],
                             close=dfpl['Close']),
              row=1, col=1)

# # # Add Bollinger Bands, EMA lines on the same subplot
# fig.add_trace(go.Scatter(x=dfpl.index, y=dfpl['BBL_30_2.0'],
#                          line=dict(color='green', width=1),
#                          name="BBL"),
#               row=1, col=1)
# fig.add_trace(go.Scatter(x=dfpl.index, y=dfpl['BBU_30_2.0'],
#                          line=dict(color='green', width=1),
#                          name="BBU"),
#               row=1, col=1)
# fig.add_trace(go.Scatter(x=dfpl.index, y=dfpl['EMA_fast'],
#                          line=dict(color='black', width=1),
#                          name="EMA_fast"),
#               row=1, col=1)
# fig.add_trace(go.Scatter(x=dfpl.index, y=dfpl['EMA_slow'],
#                          line=dict(color='blue', width=1),
#                          name="EMA_slow"),
#               row=1, col=1)

fig.add_trace(go.Scatter(x=dfpl.index, y=dfpl['Upper_Envelope'], line=dict(color='red', width=1), name="Upper Envelope"), row=1, col=1)
fig.add_trace(go.Scatter(x=dfpl.index, y=dfpl['Lower_Envelope'], line=dict(color='green', width=1), name="Lower Envelope"), row=1, col=1)

# Add markers for trade entry points on the same subplot
fig.add_trace(go.Scatter(x=dfpl.index, y=dfpl['pointpos'], mode="markers",
                         marker=dict(size=4, color="MediumPurple"),
                         name="entry"),
              row=1, col=1)

fig.update_layout(
    width=1500,
    height=1200,
    sliders=[],
    paper_bgcolor='black',  # Set the background color of the entire figure
    plot_bgcolor='black',   # Set the background color of the plotting area
    xaxis_showgrid=False,   # Remove x-axis gridlines
    yaxis_showgrid=False,   # Remove y-axis gridlines
)

fig.show()

dfopt = dfsample[0:] # copy of the dataframe
def SIGNAL():
    return dfopt.TotalSignal # 0 none 1 sell 2 buy

class MyStrat(Strategy):
    mysize = 3000 # size of trades
    slcoef = 1.1 # stop loss coeficient
    TPSLRatio = 1.5 # take profit and stop loss ratio

    def init(self): # sets strategy variables
        super().init()
        self.signal1 = self.I(SIGNAL)

    def next(self): # defines the trading strategy
        super().next()
        slatr = self.slcoef*self.data.ATR[-1] # stop loss adjusted to volatility
        TPSLRatio = self.TPSLRatio # take profit

        if self.signal1==1 and len(self.trades)==0:
            sl1 = self.data.Close[-1] - slatr
            tp1 = self.data.Close[-1] + slatr*TPSLRatio
            self.buy(sl=sl1, tp=tp1, size=self.mysize)

        elif self.signal1==2 and len(self.trades)==0:
            sl1 = self.data.Close[-1] + slatr
            tp1 = self.data.Close[-1] - slatr*TPSLRatio
            self.sell(sl=sl1, tp=tp1, size=self.mysize)

bt = Backtest(dfopt, MyStrat, cash=250, margin=1/30)
stats, heatmap = bt.optimize(slcoef=[i/10 for i in range(10, 26)],
                    TPSLRatio=[i/10 for i in range(10, 26)],
                    maximize='Return [%]', max_tries=300,
                        random_state=0,
                        return_heatmap=True)
stats

/tmp/ipykernel_11488/79916414.py:29: UserWarning:

Data index is not datetime. Assuming simple periods, but `pd.DateTimeIndex` is advised.

Backtest.optimize:   0%|          | 0/8 [00:00<?, ?it/s]

Start                                     0.0
End                                   18194.0
Duration                              18194.0
Exposure Time [%]                    9.804137
Equity Final [$]                   105.122141
Equity Peak [$]                    260.391569
Return [%]                         -57.951144
Buy & Hold Return [%]               -0.787264
Return (Ann.) [%]                         0.0
Volatility (Ann.) [%]                     NaN
Sharpe Ratio                              NaN
Sortino Ratio                             NaN
Calmar Ratio                              0.0
Max. Drawdown [%]                   -61.24997
Avg. Drawdown [%]                  -15.773511
Max. Drawdown Duration                16788.0
Avg. Drawdown Duration            2988.166667
# Trades                                 67.0
Win Rate [%]                        17.910448
Best Trade [%]                       1.105764
Worst Trade [%]                     -0.508768
Avg. Trade [%]                      -0.067063
Max. Trade Duration                     278.0
Avg. Trade Duration                 25.656716
Profit Factor                        0.638838
Expectancy [%]                      -0.066407
SQN                                  -1.50331
_strategy                 MyStrat(slcoef=2...
_equity_curve                        Equit...
_trades                       Size  EntryB...
dtype: object

import plotly.graph_objects as go
from plotly.subplots import make_subplots

def plot_trades_plotly(df, strategy):
    """Function to plot trades, including entries, exits, SL, TP using Plotly."""

    entries = strategy.entries
    exits = strategy.exits
    sl_levels = strategy.sl_levels
    tp_levels = strategy.tp_levels
    trade_types = strategy.trade_types

    # Create a plot with 1 row (main candlestick chart)
    fig = make_subplots(rows=1, cols=1, shared_xaxes=True)

    # Add candlestick plot with customized line colors
    fig.add_trace(go.Candlestick(
        x=df.index,
        open=df['Open'],
        high=df['High'],
        low=df['Low'],
        close=df['Close'],
        increasing=dict(line=dict(color='rgba(0, 255, 0, 0.6)', width=1), fillcolor='rgba(0, 255, 0, 0.6)'),
        decreasing=dict(line=dict(color='rgba(255, 0, 0, 0.6)', width=1), fillcolor='rgba(255, 0, 0, 0.6)')
    ))

    # Plot entry points
    for i, (entry_time, entry_price, trade_type) in enumerate(entries):
        color = 'green' if trade_type == 'long' else 'red'
        symbol = 'triangle-up' if trade_type == 'long' else 'triangle-down'
        fig.add_trace(go.Scatter(
            x=[entry_time], y=[entry_price],
            mode='markers',
            marker=dict(symbol=symbol, color=color, size=10),
            name=f'Entry ({trade_type.capitalize()})',
            hovertemplate=f'Entry ({trade_type.capitalize()}): %{y:.2f}<extra></extra>'
        ))

        # Add stop loss (SL) and take profit (TP) lines
        fig.add_trace(go.Scatter(
            x=[entry_time, entry_time],
            y=[sl_levels[i], tp_levels[i]],
            mode='lines',
            line=dict(dash='dash', color='rgba(255, 0, 0, 0.5)' if trade_type == 'long' else 'rgba(0, 255, 0, 0.5)'),
            hoverinfo='skip',
            showlegend=False
        ))

    # Plot exit points
    for i, (exit_time, exit_price) in enumerate(exits):
        fig.add_trace(go.Scatter(
            x=[exit_time], y=[exit_price],
            mode='markers',
            marker=dict(symbol='circle', color='orange', size=10),
            name=f'Exit',
            hovertemplate=f'Exit: %{y:.2f}<extra></extra>'
        ))

    # Add Nadaraya-Watson fitted line
    fig.add_trace(go.Scatter(
        x=df.index, y=df['NW_Fitted'],
        line=dict(color='green', width=2),
        name="Nadaraya-Watson Fit"
    ))

    # Add upper and lower envelopes (standard deviation bands)
    fig.add_trace(go.Scatter(
        x=df.index, y=df['Upper_Envelope'],
        line=dict(color='rgba(0,0,255,0.2)'),  # Light blue color for upper envelope
        name='Upper Envelope',
        showlegend=False
    ))

    fig.add_trace(go.Scatter(
        x=df.index, y=df['Lower_Envelope'],
        line=dict(color='rgba(0,0,255,0.2)'),  # Light blue color for lower envelope
        name='Lower Envelope',
        fill='tonexty',  # Fills the area between the lower envelope and the upper envelope
        fillcolor='rgba(0,0,255,0.3)',  # More opaque blue fill
        showlegend=False
    ))

    # Customize layout: set background color, gridlines, and make the chart zoom-friendly
    fig.update_layout(
        width=1500,
        height=800,
        sliders=[],
        paper_bgcolor='black',  # Background color of the entire figure
        plot_bgcolor='black',   # Background color of the plotting area
        xaxis_showgrid=False,   # Remove x-axis gridlines
        yaxis_showgrid=False,   # Remove y-axis gridlines
        xaxis_rangeslider_visible=False,  # Hide the range slider for cleaner zoom
        xaxis=dict(
            rangeselector=dict(
                buttons=list([
                    dict(count=1, label="1m", step="month", stepmode="backward"),
                    dict(count=6, label="6m", step="month", stepmode="backward"),
                    dict(step="all")
                ])
            ),
            type="category",  # Ensure better zooming when there are missing or irregular time intervals
            rangebreaks=[dict(bounds=["sat", "mon"])],  # Breaks for weekends, where no trading occurs
            rangeslider=dict(visible=True, thickness=0.1)  # Adds a thinner range slider for zoom
        ),
    )

    # Update y-axis for better zoom interaction
    fig.update_yaxes(fixedrange=False)

    # Show the plot
    fig.show()

# Example usage after running backtest
bt = Backtest(dfopt, MyStrat, cash=250, margin=1/30)
stats = bt.run()

# Get strategy instance and plot trades using the enhanced function
strategy_instance = bt._strategy
plot_trades_plotly(dfopt, strategy_instance)

/tmp/ipykernel_11488/2219177076.py:114: UserWarning:

Data index is not datetime. Assuming simple periods, but `pd.DateTimeIndex` is advised.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[27], line 119
    117 # Get strategy instance and plot trades using the enhanced function
    118 strategy_instance = bt._strategy
--> 119 plot_trades_plotly(dfopt, strategy_instance)

Cell In[27], line 7, in plot_trades_plotly(df, strategy)
      4 def plot_trades_plotly(df, strategy):
      5     """Function to plot trades, including entries, exits, SL, TP using Plotly."""
----> 7     entries = strategy.entries
      8     exits = strategy.exits
      9     sl_levels = strategy.sl_levels

AttributeError: type object 'MyStrat' has no attribute 'entries'

stats["_strategy"]

<Strategy MyStrat(slcoef=2.0,TPSLRatio=2.1)>

# Convert multiindex series to dataframe
heatmap_df = heatmap.unstack()
plt.figure(figsize=(10, 8))
sns.heatmap(heatmap_df, annot=True, cmap='viridis', fmt='.0f')
plt.show()

dftest = df[:]
def SIGNAL():
    return dftest.TotalSignal

class MyStrat(Strategy):
    mysize = 3000
    slcoef = 2.6
    TPSLRatio = 2.6

    def init(self):
        super().init()
        self.signal1 = self.I(SIGNAL)

    def next(self):
        super().next()
        slatr = self.slcoef*self.data.ATR[-1]
        TPSLRatio = self.TPSLRatio

        if self.signal1==1 and len(self.trades)==0:
            sl1 = self.data.Close[-1] - slatr
            tp1 = self.data.Close[-1] + slatr*TPSLRatio
            self.buy(sl=sl1, tp=tp1, size=self.mysize)

        elif self.signal1==2 and len(self.trades)==0:
            sl1 = self.data.Close[-1] + slatr
            tp1 = self.data.Close[-1] - slatr*TPSLRatio
            self.sell(sl=sl1, tp=tp1, size=self.mysize)

bt = Backtest(dftest, MyStrat, cash=250, margin=1/30, commission=0.0002)
bt.run()

/tmp/ipykernel_11488/1347927932.py:29: UserWarning:

Data index is not datetime. Assuming simple periods, but `pd.DateTimeIndex` is advised.

Start                                     0.0
End                                   18194.0
Duration                              18194.0
Exposure Time [%]                   18.777509
Equity Final [$]                    92.970842
Equity Peak [$]                    264.467407
Return [%]                         -62.811663
Buy & Hold Return [%]               -0.787264
Return (Ann.) [%]                         0.0
Volatility (Ann.) [%]                     NaN
Sharpe Ratio                              NaN
Sortino Ratio                             NaN
Calmar Ratio                              0.0
Max. Drawdown [%]                  -64.846012
Avg. Drawdown [%]                  -11.245286
Max. Drawdown Duration                16272.0
Avg. Drawdown Duration                 1792.2
# Trades                                 60.0
Win Rate [%]                             15.0
Best Trade [%]                       1.507011
Worst Trade [%]                     -0.679918
Avg. Trade [%]                      -0.081148
Max. Trade Duration                     764.0
Avg. Trade Duration                 56.016667
Profit Factor                        0.650433
Expectancy [%]                      -0.079987
SQN                                 -1.287177
_strategy                             MyStrat
_equity_curve                        Equit...
_trades                       Size  EntryB...
dtype: object

bt.plot()

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    at Module._resolveFilename (node:internal/modules/cjs/loader:1145:15)
    at Module._load (node:internal/modules/cjs/loader:986:27)
    at Module.require (node:internal/modules/cjs/loader:1233:19)
    at require (node:internal/modules/helpers:179:18)
    at i (/root/.vscode-server/bin/fee1edb8d6d72a0ddff41e5f71a671c23ed924b9/out/server-cli.js:3:98)
    at r.load (/root/.vscode-server/bin/fee1edb8d6d72a0ddff41e5f71a671c23ed924b9/out/server-cli.js:2:1637)
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    at Object.errorback (/root/.vscode-server/bin/fee1edb8d6d72a0ddff41e5f71a671c23ed924b9/out/server-cli.js:3:9457)
    at h.triggerErrorback (/root/.vscode-server/bin/fee1edb8d6d72a0ddff41e5f71a671c23ed924b9/out/server-cli.js:1:14252)
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    at /root/.vscode-server/bin/fee1edb8d6d72a0ddff41e5f71a671c23ed924b9/out/server-cli.js:1:132
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    at node:internal/main/run_main_module:28:49 {
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Here are the modules that depend on it:
[ 'fs' ]

def gaussian_kernel(x, bandwidth):
    return (1 / (np.sqrt(2 * np.pi) * bandwidth)) * np.exp(-0.5 * ((x / bandwidth) ** 2))

def compute_weights(X, x, bandwidth):
    weights = [gaussian_kernel(x - i, bandwidth) for i in X]
    weights /= np.sum(weights) # Normalize to make the weights sum to 1
    return weights

# Generate synthetic price data
np.random.seed(0)
prices = np.array([2 for i in range(0, 100)])  # 100 price points

# Select the current price point (most recent)
current_price_index = 99  # Last price in the series
current_price = prices[current_price_index]

# Bandwidth (standard deviation of the kernel)
bandwidth = 10

# Compute weights for all preceding points
weights = compute_weights(range(0, 99), current_price_index, bandwidth)

# Plot the weights distribution
plt.figure(figsize=(10, 5))
plt.bar(range(current_price_index), weights, color='blue')
plt.title('Weight Distribution for Nadaraya-Watson Estimator')
plt.xlabel('Index of Price Points')
plt.ylabel('Weight')
plt.grid(False)
plt.gca().set_facecolor('black') # Change background color of the plot to black
plt.show()

	Gmt time	Open	High	Low	Close	Volume	EMA_slow	EMA_fast	ATR
0	2023-02-01 00:00:00	1.08605	1.08619	1.08583	1.08604	2184.41	NaN	NaN	NaN
1	2023-02-01 00:15:00	1.08604	1.08623	1.08583	1.08609	2373.76	NaN	NaN	NaN
2	2023-02-01 00:30:00	1.08608	1.08637	1.08604	1.08630	1649.21	NaN	NaN	NaN
3	2023-02-01 00:45:00	1.08629	1.08638	1.08606	1.08622	2071.47	NaN	NaN	NaN
4	2023-02-01 01:00:00	1.08622	1.08634	1.08594	1.08607	2021.66	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...
18190	2024-02-16 20:45:00	1.07744	1.07770	1.07729	1.07764	3288.91	1.076952	1.077122	0.000443
18191	2024-02-16 21:00:00	1.07764	1.07771	1.07747	1.07763	1677.19	1.076979	1.077147	0.000414
18192	2024-02-16 21:15:00	1.07763	1.07778	1.07758	1.07776	964.40	1.077010	1.077176	0.000384
18193	2024-02-16 21:30:00	1.07777	1.07778	1.07761	1.07772	1957.77	1.077038	1.077203	0.000353
18194	2024-02-16 21:45:00	1.07773	1.07775	1.07747	1.07749	1693.33	1.077055	1.077217	0.000343

	Gmt time	Open	High	Low	Close	Volume	EMA_slow	EMA_fast	ATR	NW_Fitted	Upper_Envelope	Lower_Envelope
0	2023-02-01 00:00:00	1.08605	1.08619	1.08583	1.08604	2184.41	NaN	NaN	NaN	1.086118	1.087100	1.085137
1	2023-02-01 00:15:00	1.08604	1.08623	1.08583	1.08609	2373.76	NaN	NaN	NaN	1.086109	1.087091	1.085127
2	2023-02-01 00:30:00	1.08608	1.08637	1.08604	1.08630	1649.21	NaN	NaN	NaN	1.086089	1.087071	1.085107
3	2023-02-01 00:45:00	1.08629	1.08638	1.08606	1.08622	2071.47	NaN	NaN	NaN	1.086055	1.087037	1.085073
4	2023-02-01 01:00:00	1.08622	1.08634	1.08594	1.08607	2021.66	NaN	NaN	NaN	1.086005	1.086987	1.085023
...	...	...	...	...	...	...	...	...	...	...	...	...
95	2023-02-01 23:45:00	1.10115	1.10129	1.10091	1.10124	1534.00	1.094777	1.095766	0.001213	1.101290	1.102272	1.100308
96	2023-02-02 00:00:00	1.10123	1.10182	1.10102	1.10151	4177.33	1.095041	1.096046	0.001154	1.101447	1.102428	1.100465
97	2023-02-02 00:15:00	1.10151	1.10218	1.10126	1.10192	2184.37	1.095311	1.096333	0.001120	1.101539	1.102521	1.100557
98	2023-02-02 00:30:00	1.10191	1.10237	1.10152	1.10220	4100.23	1.095581	1.096619	0.001082	1.101579	1.102561	1.100597
99	2023-02-02 00:45:00	1.10223	1.10266	1.10185	1.10185	4133.15	1.095827	1.096874	0.001043	1.101579	1.102561	1.100597

	Gmt time	Open	High	Low	Close	Volume	EMA_slow	EMA_fast	ATR	NW_Fitted	Upper_Envelope	Lower_Envelope	BBL_30_2.0	BBM_30_2.0	BBU_30_2.0	BBB_30_2.0	BBP_30_2.0	EMASignal
0	2023-02-01 00:00:00	1.08605	1.08619	1.08583	1.08604	2184.41	NaN	NaN	NaN	1.086118	1.087100	1.085137	NaN	NaN	NaN	NaN	NaN	0
1	2023-02-01 00:15:00	1.08604	1.08623	1.08583	1.08609	2373.76	NaN	NaN	NaN	1.086109	1.087091	1.085127	NaN	NaN	NaN	NaN	NaN	0
2	2023-02-01 00:30:00	1.08608	1.08637	1.08604	1.08630	1649.21	NaN	NaN	NaN	1.086089	1.087071	1.085107	NaN	NaN	NaN	NaN	NaN	0
3	2023-02-01 00:45:00	1.08629	1.08638	1.08606	1.08622	2071.47	NaN	NaN	NaN	1.086055	1.087037	1.085073	NaN	NaN	NaN	NaN	NaN	0
4	2023-02-01 01:00:00	1.08622	1.08634	1.08594	1.08607	2021.66	NaN	NaN	NaN	1.086005	1.086987	1.085023	NaN	NaN	NaN	NaN	NaN	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18190	2024-02-16 20:45:00	1.07744	1.07770	1.07729	1.07764	3288.91	1.076952	1.077122	0.000443	1.077652	1.078634	1.076670	1.074489	1.076929	1.079370	0.453217	0.645604	2
18191	2024-02-16 21:00:00	1.07764	1.07771	1.07747	1.07763	1677.19	1.076979	1.077147	0.000414	1.077638	1.078620	1.076657	1.074836	1.077047	1.079259	0.410649	0.631739	2
18192	2024-02-16 21:15:00	1.07763	1.07778	1.07758	1.07776	964.40	1.077010	1.077176	0.000384	1.077634	1.078616	1.076652	1.075116	1.077151	1.079187	0.377894	0.649531	2
18193	2024-02-16 21:30:00	1.07777	1.07778	1.07761	1.07772	1957.77	1.077038	1.077203	0.000353	1.077632	1.078614	1.076651	1.075506	1.077264	1.079021	0.326276	0.629830	2
18194	2024-02-16 21:45:00	1.07773	1.07775	1.07747	1.07749	1693.33	1.077055	1.077217	0.000343	1.077631	1.078613	1.076649	1.075802	1.077347	1.078891	0.286670	0.546410	2

1. Import test data¶

2. Load in the data¶

3. Define the EMAs and ATR¶

4. Define Nadaraya Watson Strategy¶

5. Define the Bolinger Bands Calculation¶

6. Nadaraya Watson Envelopes Visualization¶

7. Signals based on EMA¶

8. Calculate total signal for the Nadaraya Watson envelope.¶

Point of the total signal which takes under the consideration EMA and Nadaraya Watson envelope.¶

Visualise the total signal including the EMA and Nadaraya Watson envelope.¶

9. Back test the Nadaraya Watson envelope strategy.¶